Generalizable Neural Radiance Field

Generalizable Neural Radiance Fields (NeRFs) aim to create 3D scene representations capable of synthesizing novel views of unseen scenes, overcoming the limitations of traditional NeRFs which require per-scene training. Current research focuses on improving generalization ability through advanced feature aggregation techniques (e.g., incorporating 3D context, visibility information, and epipolar geometry), novel architectures (like transformers and wavelet-based methods), and robust point-based representations. These advancements are significant for applications such as augmented and virtual reality, robotics, and 3D modeling, enabling more efficient and realistic scene rendering from limited input data.